面向雷电预报的雷达、卫星遥感资料同化及其应用初步研究
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摘要
以面向雷电预报的雷达、卫星遥感资料同化及其应用为研究目的,利用NCEP资料、新一代多普勒雷达网、FY2C气象卫星、闪电定位仪网、自动气象站等观测资料的诊断、同化模拟以及理论分析等方法,对气象遥感资料的同化设计及其在雷暴天气条件下雷电预测方面的应用进行了综合研究和初步分析。
主要得到了以下结论:
1)雷达资料间接同化方面:以夏季暴雨为研究对象,用MUSCAT技术做双多普勒雷达风场反演资料的间接同化,与非同化实验相比,同化风场反演资料的实验能够对初始风场做有效调整;随着积分的进行,可预报出风场、温度场等的中尺度结构。但总体来说,对湿度场的调整并不显著,而且受双雷达反演区域较小的限制,短时降水模拟结果与实况有出入。但同化实验对于主要降水中心的反映仍然比非同化实验好得多,对于初始场调整、模式预报仍然起到了积极作用。
2)雷达资料直接同化方面:以夏季暴雨为研究对象,在仅有NCEP资料的情况下,通过ARPS-3DVar和ADAS同化系统直接同化多普勒雷达资料可以对气象要素场产生显著影响和改善。多普勒雷达资料的同化时次越多,对于风场、水汽场等的调整与实际观测越接近,尤其是水汽场,使得短时降水模拟与实况越吻合;直接同化结果比间接同化显著。
其中,径向风资料对初始风场调整显著,使中尺度特征明显地表现出来;当积分开始进行后(如1h),径向风资料同化实验的水汽场出现大值中心,但这种水汽条件与实际不符。
反射率因子资料对初始水汽场调整明显,在风暴发生处产生清晰的水汽中心;当积分进行1h后,反射率因子资料同化实验对风场的调整已经相对清晰,与此时的径向风同化实验类似,基本可以模拟出风暴的中尺度风特征,并且保持。
个例实验说明反射率因子资料在改进模式预报场方面更加显著。
3)卫星资料间接同化方面:分别用TCFM导风技术和国家局下发的云导风资料,进行了卫星资料间接同化实验。以台风个例作为研究对象,TCFM云导风资料同化实验对初始的垂直风场调整显著,不论是低、中、高层,在台风眼壁区域均出现强烈的上升运动,且这种特征一直持续数小时。配合此处模式模拟得到的水汽中心,为台风眼壁区的强风暴、降雨创造了条件。同化实验对于台风眼区的螺旋形雨带贡献显著,说明TCFM云导风资料同化实验不仅有效调整了模式的垂直速度场,还对降水模拟有了明显改善。
4)通过理论分析,利用云粒子与极化降水粒子碰撞并弹离的感应起电机制和较大过冷云滴与雹粒碰冻并产生冰屑的温差起电机制下得到的E-I_r,由雷达、卫星遥感资料直接和间接同化模拟得到I_r,来反推E,了解雷暴云中闪电强度的平面分布,设置放电阈值为300KV/m,推算出放电的位置和首次发生闪电的时间。计算的闪电发生的时间和位置无法区分是IC还是CG。但计算结果和LLS观测资料也有可比性。
5)个例分析表明,自动气象站实测的降水和LLS观测的CG资料对比分析结果为:二者在时、空同时配对出现的几率很小;降雨与-CG所占比例关系较为密切,二者峰谷配合,在时间上-CG峰值出现时间一般比降雨早10min左右。+CG则可能与雷暴过程中的龙卷、冰雹等强天气过程相关。
Herein main purpose is preliminary study on CINRAD (Chinese Next Generation Weather Radar) and satellite data assimilation and applications oriented to lightning storm forecast. Based on data of NCEP, CINRAD net, FY2C meteorological satellite, LLS(Lightning Location System) nets, AWS(Automatic Weather Station) nets, remote sensing observations assimiliaton and lightning storm forecast are studied and analyzed preliminarily with diagnosis, numerical simulation and theories, leading to the following main results.
1) Radar data indirect assimilation experiments: for rainstorm forecast, dual-Doppler radar radial velocity data through MUSCAT (Multiple-Doppler Synthesis and Continuity Adjustment Technique) method are assimilated into numerical model ARPS. Compared with non-assimilation experiment, initial wind fields are improved effectively. Followed with integral time, indirect assimilation experiments can predict mesoscale structures of wind, temperature and so on. While as a whole, adjusted humidity fields are not very clear, furthermore, the retrieval area limit of dual-Doppler radar is exit, all of these result in simulated rainfall locations are different from observation in situ. While indirect assimilation experiment is better than that of non-assimilation one in rainfall center, the former appears positive role.
2) Radar observations direct assimilation experiments: By means of ARPS-3DVar and ADAS (ARPS Data Analysis/Assimilation System), CINRAD observations are assimilated only with NCEP reanalysis data for the forecast of rainstorm. Results show that meteorological element fields are improved observably by assimilation experiments. And more assimilation times lead to better wind field, water vapor field and the like, which are closer to observations in situ, especially for water vapor field, leading to the fact that short-time precipitation simulation is also closer to observations. All of these results are better than that of indirect assimilation experiments.
Thereinto radar radial velocity data improve mostly upon initial wind field with obvious mesoscale structures. The corresponding assimilation experiment improves effectively on water vapor field, causing clear water vapor center, just in actual rainstorm center. After 1h integral, reflectivity data's role on wind field is clear, and mesoscale structure can be simulated continually, similarly with radial velocity data assimilation experiment.
For radial velocity data, followed with the beginning of integral, for example, after one hour, radial velocity data assimilation experiment appears water vapor centers with great values, but these are not concord with actual condition.
It is showed that reflectivity data assimilation experiments are briefly marked on improving forecast fields than that of radial velocity.
3) FY2C satellite observations indirect assimilation experiments: CMW (Cloud Motion Wind) data from TCFM (Tracking Cloud with Combined Fourier Phase Analysis and maximum Correlation) method and CMA (China Meteorological Administration) released CMW data are assimilated into numerical model respectively. For the example of typhoon, CMW data from TCFM method assimilation experiment adjusts effectively on initial vertical wind field, resulting intense ascent motions at eye wall of the typhoon MATSA, whatever in lower, middle or upper levels, lasting out for hours. These ascent motions are just at the center of modeled water vapor fields, which leading to severe storm and rainfall at eye wall of typhoon. It is presented that indirect assimilation experiment of CMW data from TCFM method improves effectively not only on vertical velocity field but for precipitation.
4) Based on theory analysis, E-I_r relationship is brought forward with two electrification mechanisms. One is inductive mechanism, i.e., cloud particles collide with precipitation particles then flick away, the other is noninductive mechanism because of different temperature, i.e., bigger supercooled cloud particles collide and freeze with hail particles combined with ice scraps. By means of CINRAD and FY2C data assimilation, precipitation intensity I_r can be obtained, then electric field intensity E, discharge time and locations can be achieved with supposed threshold 300KV/m. Though it can not distinguish between IC (Intra-Cloud) lightning and CG (Cloud-to-Ground) lightning, results are still comparable to LLS observations.
5) Example analysis shows that the relationship between precipitation data from AMS and CG data from LLS is not very clear. The probability of the two appears at the same time period and same location is very small. Rainfall is closer to -CG than that of +CQ their crests and troughs match, crests and troughs of -CG are earlier than that of rainfall, roughly 10min. +CG maybe relative to severe storm like spout, hail and the like.
主要得到了以下结论:
1)雷达资料间接同化方面:以夏季暴雨为研究对象,用MUSCAT技术做双多普勒雷达风场反演资料的间接同化,与非同化实验相比,同化风场反演资料的实验能够对初始风场做有效调整;随着积分的进行,可预报出风场、温度场等的中尺度结构。但总体来说,对湿度场的调整并不显著,而且受双雷达反演区域较小的限制,短时降水模拟结果与实况有出入。但同化实验对于主要降水中心的反映仍然比非同化实验好得多,对于初始场调整、模式预报仍然起到了积极作用。
2)雷达资料直接同化方面:以夏季暴雨为研究对象,在仅有NCEP资料的情况下,通过ARPS-3DVar和ADAS同化系统直接同化多普勒雷达资料可以对气象要素场产生显著影响和改善。多普勒雷达资料的同化时次越多,对于风场、水汽场等的调整与实际观测越接近,尤其是水汽场,使得短时降水模拟与实况越吻合;直接同化结果比间接同化显著。
其中,径向风资料对初始风场调整显著,使中尺度特征明显地表现出来;当积分开始进行后(如1h),径向风资料同化实验的水汽场出现大值中心,但这种水汽条件与实际不符。
反射率因子资料对初始水汽场调整明显,在风暴发生处产生清晰的水汽中心;当积分进行1h后,反射率因子资料同化实验对风场的调整已经相对清晰,与此时的径向风同化实验类似,基本可以模拟出风暴的中尺度风特征,并且保持。
个例实验说明反射率因子资料在改进模式预报场方面更加显著。
3)卫星资料间接同化方面:分别用TCFM导风技术和国家局下发的云导风资料,进行了卫星资料间接同化实验。以台风个例作为研究对象,TCFM云导风资料同化实验对初始的垂直风场调整显著,不论是低、中、高层,在台风眼壁区域均出现强烈的上升运动,且这种特征一直持续数小时。配合此处模式模拟得到的水汽中心,为台风眼壁区的强风暴、降雨创造了条件。同化实验对于台风眼区的螺旋形雨带贡献显著,说明TCFM云导风资料同化实验不仅有效调整了模式的垂直速度场,还对降水模拟有了明显改善。
4)通过理论分析,利用云粒子与极化降水粒子碰撞并弹离的感应起电机制和较大过冷云滴与雹粒碰冻并产生冰屑的温差起电机制下得到的E-I_r,由雷达、卫星遥感资料直接和间接同化模拟得到I_r,来反推E,了解雷暴云中闪电强度的平面分布,设置放电阈值为300KV/m,推算出放电的位置和首次发生闪电的时间。计算的闪电发生的时间和位置无法区分是IC还是CG。但计算结果和LLS观测资料也有可比性。
5)个例分析表明,自动气象站实测的降水和LLS观测的CG资料对比分析结果为:二者在时、空同时配对出现的几率很小;降雨与-CG所占比例关系较为密切,二者峰谷配合,在时间上-CG峰值出现时间一般比降雨早10min左右。+CG则可能与雷暴过程中的龙卷、冰雹等强天气过程相关。
Herein main purpose is preliminary study on CINRAD (Chinese Next Generation Weather Radar) and satellite data assimilation and applications oriented to lightning storm forecast. Based on data of NCEP, CINRAD net, FY2C meteorological satellite, LLS(Lightning Location System) nets, AWS(Automatic Weather Station) nets, remote sensing observations assimiliaton and lightning storm forecast are studied and analyzed preliminarily with diagnosis, numerical simulation and theories, leading to the following main results.
1) Radar data indirect assimilation experiments: for rainstorm forecast, dual-Doppler radar radial velocity data through MUSCAT (Multiple-Doppler Synthesis and Continuity Adjustment Technique) method are assimilated into numerical model ARPS. Compared with non-assimilation experiment, initial wind fields are improved effectively. Followed with integral time, indirect assimilation experiments can predict mesoscale structures of wind, temperature and so on. While as a whole, adjusted humidity fields are not very clear, furthermore, the retrieval area limit of dual-Doppler radar is exit, all of these result in simulated rainfall locations are different from observation in situ. While indirect assimilation experiment is better than that of non-assimilation one in rainfall center, the former appears positive role.
2) Radar observations direct assimilation experiments: By means of ARPS-3DVar and ADAS (ARPS Data Analysis/Assimilation System), CINRAD observations are assimilated only with NCEP reanalysis data for the forecast of rainstorm. Results show that meteorological element fields are improved observably by assimilation experiments. And more assimilation times lead to better wind field, water vapor field and the like, which are closer to observations in situ, especially for water vapor field, leading to the fact that short-time precipitation simulation is also closer to observations. All of these results are better than that of indirect assimilation experiments.
Thereinto radar radial velocity data improve mostly upon initial wind field with obvious mesoscale structures. The corresponding assimilation experiment improves effectively on water vapor field, causing clear water vapor center, just in actual rainstorm center. After 1h integral, reflectivity data's role on wind field is clear, and mesoscale structure can be simulated continually, similarly with radial velocity data assimilation experiment.
For radial velocity data, followed with the beginning of integral, for example, after one hour, radial velocity data assimilation experiment appears water vapor centers with great values, but these are not concord with actual condition.
It is showed that reflectivity data assimilation experiments are briefly marked on improving forecast fields than that of radial velocity.
3) FY2C satellite observations indirect assimilation experiments: CMW (Cloud Motion Wind) data from TCFM (Tracking Cloud with Combined Fourier Phase Analysis and maximum Correlation) method and CMA (China Meteorological Administration) released CMW data are assimilated into numerical model respectively. For the example of typhoon, CMW data from TCFM method assimilation experiment adjusts effectively on initial vertical wind field, resulting intense ascent motions at eye wall of the typhoon MATSA, whatever in lower, middle or upper levels, lasting out for hours. These ascent motions are just at the center of modeled water vapor fields, which leading to severe storm and rainfall at eye wall of typhoon. It is presented that indirect assimilation experiment of CMW data from TCFM method improves effectively not only on vertical velocity field but for precipitation.
4) Based on theory analysis, E-I_r relationship is brought forward with two electrification mechanisms. One is inductive mechanism, i.e., cloud particles collide with precipitation particles then flick away, the other is noninductive mechanism because of different temperature, i.e., bigger supercooled cloud particles collide and freeze with hail particles combined with ice scraps. By means of CINRAD and FY2C data assimilation, precipitation intensity I_r can be obtained, then electric field intensity E, discharge time and locations can be achieved with supposed threshold 300KV/m. Though it can not distinguish between IC (Intra-Cloud) lightning and CG (Cloud-to-Ground) lightning, results are still comparable to LLS observations.
5) Example analysis shows that the relationship between precipitation data from AMS and CG data from LLS is not very clear. The probability of the two appears at the same time period and same location is very small. Rainfall is closer to -CG than that of +CQ their crests and troughs match, crests and troughs of -CG are earlier than that of rainfall, roughly 10min. +CG maybe relative to severe storm like spout, hail and the like.
引文
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